Σύγκριση μεθόδων

	Ανάλυση Κύριων Συνιστωσών	Εκτίμηση Εύρωστων Συνδιακυμάνσεων (MCD)
Πεδίο	Μηχανική Μάθηση	Στατιστική
Οικογένεια	Machine learning	Regression model
Έτος προέλευσης	2002	1999
Δημιουργός	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Τύπος	Unsupervised dimensionality reduction	Robust multivariate location-scatter estimator
Θεμελιώδης πηγή	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Εναλλακτικές ονομασίες	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Συναφείς	3	4
Σύνοψη	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.	Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation.